Multiview data analysis has become increasingly important for integrating heterogeneous information from multiple data sources. Cooperative learning provides a flexible framework for multiview integration by bridging early and late fusion strategies. However, its use of squared loss makes it sensitive to outliers. In t...
Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using thres...
Yu-Long Li, Ke Wan, Toshio Shimokawa et al.· 0 citations
This work proposes Cooperative Learning with a penalized Linear Mixed Model (CL-pLMM) for high-dimensional multiview data with a clustered structure and shows that its objective function can be represented as a penalized linear mixed-effects model applied to augmented data, allowing existing estimation procedures to be...
This work proposes geographically regularized AUC-maximizing personalized federated learning (GrAUC-PFL), which directly optimizes a smooth pairwise AUC surrogate to learn personalized models while keeping patient-level data local and accounting for institutional heterogeneity.
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa· 0 citations
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